CodeAlpha_Netflix_Content_Analysis

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📌 Project Overview

This project presents a comprehensive Exploratory Data Analysis (EDA) of the Netflix Titles dataset using Python. The objective is to uncover meaningful insights about Netflix's content library by analyzing trends in movies, TV shows, genres, countries, ratings, release years, and content growth.

The project demonstrates the complete data analytics workflow, including data cleaning, preprocessing, visualization, business insights, and dashboard creation.

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🎯 Objectives

• Understand the structure and quality of the dataset.
• Perform data cleaning and preprocessing.
• Identify missing values and data inconsistencies.
• Analyze the distribution of Movies and TV Shows.
• Discover the most popular genres and ratings.
• Analyze country-wise content production.
• Study Netflix's content growth over time.
• Generate actionable business insights.
• Build a dashboard summarizing the analysis.

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📂 Project Structure

CodeAlpha_Netflix_Content_EDA
│
├── Dashboard
│   ├── dashboard.ipynb
│   └── netflix_analysis_dashboard.png
│
├── Data
│   └── netflix_titles.csv
│
├── Images
│   ├── content_added_year.png
│   ├── content_rating_distribution.png
│   ├── correlation_matrix.png
│   ├── dashboard_summary.png
│   ├── genre_wordcloud.png
│   ├── missing_values_heatmap.png
│   ├── monthly_addition.png
│   ├── movie_tv_growth.png
│   ├── movie_vs_tvshows.png
│   ├── netflix_analysis_dashboard.png
│   ├── release_year_trend.png
│   ├── top10_actors.png
│   ├── top10_countries.png
│   ├── top10_directors.png
│   └── top10_genres.png
│
├── Notebooks
│   └── EDA.ipynb
│
├── Reports
│
├── README.md
├── requirements.txt
└── .gitignore

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🛠 Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• WordCloud
• Jupyter Notebook

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📊 Dataset Information

Dataset: Netflix Titles Dataset

The dataset contains information about Netflix Movies and TV Shows, including:

• Show ID
• Content Type
• Title
• Director
• Cast
• Country
• Date Added
• Release Year
• Rating
• Duration
• Genres
• Description

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🔍 Data Analysis Performed

✔ Data Cleaning

• Removed empty columns
• Checked missing values
• Checked duplicate records
• Converted date columns
• Feature Engineering

✔ Exploratory Data Analysis

• Movies vs TV Shows
• Content Rating Distribution
• Top Producing Countries
• Release Year Trend
• Content Added by Year
• Monthly Content Addition
• Top Genres
• Top Directors
• Top Actors
• Movie vs TV Show Growth
• Missing Values Heatmap
• Correlation Matrix
• Genre Word Cloud

✔ Dashboard

A dashboard was created using Matplotlib to summarize important business insights.

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📈 Key Findings

• Netflix contains significantly more Movies than TV Shows.
• TV-MA is the most common content rating.
• The United States contributes the largest amount of content.
• India is the second-largest content producer.
• International Movies dominate the platform.
• Drama and Comedy are the most popular genres.
• Netflix experienced rapid content growth after 2015.
• Content additions peaked around 2019.
• Movies have consistently outnumbered TV Shows over the years.
• The dataset contains missing values mainly in Director, Cast, and Country.

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💡 Business Recommendations

• Increase investment in International content.
• Continue producing Drama and Comedy titles.
• Expand family-friendly content offerings.
• Focus on emerging markets with high content demand.
• Collaborate with directors having strong success records.
• Improve metadata quality for missing director and cast information.

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📊 Dashboard Preview

Images/netflix_analysis_dashboard.png

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📸 Sample Visualizations

Dashboard Preview

Movies vs TV Shows

Genre Word Cloud

Top Countries

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🚀 How to Run

Clone Repository

git clone https://github.com/kindlefest08/CodeAlpha_Netflix_Content_Analysis.git

Navigate

cd CodeAlpha_EDA

Install Requirements

pip install -r requirements.txt

Launch Jupyter Notebook

jupyter notebook

Open:

Notebooks/EDA.ipynb

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📌 Future Improvements

• Interactive Plotly Dashboard
• Power BI Dashboard
• Netflix Recommendation System
• Sentiment Analysis on Netflix Reviews
• Machine Learning for Content Prediction

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👩‍💻 Author

Vaishali Limbapure

Computer Engineering Student

Passionate about Data Analytics, Machine Learning, and Artificial Intelligence.

GitHub: https://github.com/kindlefest08

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